{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: POD-DL-ROM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "os.environ[\"KERAS_BACKEND\"] = \"jax\"\n",
    "os.environ[\"XLA_FLAGS\"] = \\\n",
    "    '--xla_gpu_deterministic_ops=true --xla_gpu_autotune_level=0'\n",
    "\n",
    "import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.insert(0, '..')\n",
    "from src.dataset import Dataset\n",
    "from src.utils import *\n",
    "from src import Trainer\n",
    "import src.nns as nns\n",
    "\n",
    "keras.backend.clear_session()\n",
    "keras.utils.set_random_seed(1)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data generation\n",
    "For the sake of simplicity, we consider the following parametrized PDE depending on $\\mu \\in [2,3]$,\n",
    "\n",
    "\\begin{equation}\n",
    "\\left\\{\n",
    "\\begin{aligned}\n",
    "- \\Delta u &= \\mu^2 sin(\\mu x), & x \\in (0,2\\pi) \\\\\n",
    "u &= \\sin(\\mu x), &x \\in \\{0,2\\pi\\},\n",
    "\\end{aligned}\n",
    "\\right.\n",
    "\\end{equation}\n",
    "\n",
    "whose exact solution is available analytically, that is, $u(x,\\mu) = \\sin(\\mu x)$. Thus, we generate a set of snapshots varying $\\mu$ and choosing $x$ on an equi-spaced grid, thus obtaining the snapshots map $ \\{\\mathbf{u}_h(\\mu)\\}_{j=1}^{N_s} \\in \\mathbb{R}^{N_s \\times N_h}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set problem data and configuration \n",
    "p = 1\n",
    "N_h = 1205\n",
    "N_s = 400\n",
    "latent_dim = 3\n",
    "n_channels = 1\n",
    "pod_dim = 5\n",
    "n_train = int(0.8 * N_s)\n",
    "n_test = int(0.1 * N_s)\n",
    "\n",
    "# Generate data\n",
    "X = np.linspace(0, 2*np.pi, N_h)\n",
    "MU = 2 + np.random.rand(N_s,p)\n",
    "U = np.sin(np.einsum('ic,j->ijc', MU, X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary dimensionality reduction through POD\n",
    "POD-DL-ROMs entail a preliminary dimensionality reduction step that allows the user to cast the learning problem at a reduced level, thus improving the both the convergence and the computational performance of the training. \n",
    "\n",
    "Indeed, we consider the low-rank based architecture $\\mu \\mapsto \\mathbf{V} \\hat{\\mathbf{q}}(\\mu)$,\n",
    "where $\\mathbf{V} \\in \\mathbb{R}^{N_h \\times pod\\_dim}$ is the POD matrix and $\\mu \\mapsto \\hat{\\mathbf{q}}(\\mu) \\in \\mathbb{R}^{pod\\_dim}$ is a suitable neural network that approximates the POD coefficients. \n",
    "\n",
    "Thus, thanks to the (semi-)orthonormality of the POD matrix, we cast the approximation problem at a POD level $\\mathbf{V}^T \\mathbf{V} \\hat{\\mathbf{q}} \\approx \\mathbf{V}^T \\mathbf{u}_h \\implies \\hat{\\mathbf{q}} \\approx \\mathbf{V}^T \\mathbf{u}_h = \\mathbf{q}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-23 18:23:00.244171: W external/xla/xla/service/gpu/nvptx_compiler.cc:698] The NVIDIA driver's CUDA version is 11.6 which is older than the ptxas CUDA version (11.8.89). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POD projection error = 3.769e-03\n"
     ]
    }
   ],
   "source": [
    "# Compute POD on data\n",
    "pod = POD(pod_dim = pod_dim, n_channels = n_channels, rpod = True)\n",
    "pod.compute(snapshot_matrix = U[:n_train])\n",
    "Q = pod.project(u_hf = U)\n",
    "\n",
    "# Collect data pairs\n",
    "train_data = {'mu' : MU[:n_train], 'q_true' : Q[:n_train]}\n",
    "val_data = {'mu' : MU[n_train:-n_test], 'q_true' : Q[n_train:-n_test]}\n",
    "test_data = {'mu' : MU[-n_test:], 'u_true' : U[-n_test:]}\n",
    "\n",
    "# Compute POD projection error to assess the efficacy of the preliminary \n",
    "# dimensionality reduction\n",
    "projection_error = pod.compute_projection_error(test_data['u_true'])\n",
    "print(\"POD projection error = %1.3e\" % projection_error)\n",
    "\n",
    "# Costruct datasets used for training\n",
    "train_dataset = Dataset(\n",
    "    batched_data_ids = list(train_data.keys()),\n",
    "    sup_data = train_data,\n",
    "    batch_size_sup = 8\n",
    ")\n",
    "val_dataset = Dataset(\n",
    "    batched_data_ids = list(val_data.keys()),\n",
    "    sup_data = val_data,\n",
    "    batch_size_sup = 8\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DL-ROM core design\n",
    "Within the next code section, we focus on the definition of the neural network architecture $\\mu \\mapsto \\hat{\\mathbf{q}}(\\mu) \\in \\mathbb{R}^{pod\\_dim}$. \n",
    "\n",
    "Specifically, we design the neural network core by suitably defining a DL-ROM consisting of a reduced network, an encoder and a decoder. \n",
    "\n",
    "We remark that $\\mu \\mapsto \\hat{\\mathbf{q}}(\\mu) = \\textnormal{dec} \\circ \\textnormal{red}(\\mu) \\approx \\mathbf{q}(\\mu)$, and the encoder is only used to ensure a suitable latent representation, namely, $\\textnormal{enc}(\\mathbf{q}(\\mu)) \\approx \\textnormal{red}(\\mu)$. Thus, the encoder can be discarded at inference time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define reduce network\n",
    "reduced_network = nns.DenseNetwork(\n",
    "    width = 30, \n",
    "    depth = 3, \n",
    "    output_dim = latent_dim,\n",
    "    activation = 'elu',\n",
    "    kernel_initializer = 'he_uniform'\n",
    ")\n",
    "\n",
    "# Define encoder\n",
    "encoder_network = nns.DenseNetwork(\n",
    "    width = 30, \n",
    "    depth = 2, \n",
    "    output_dim = latent_dim,\n",
    "    activation = 'elu',\n",
    "    kernel_initializer = 'he_uniform'\n",
    ")\n",
    "reshape_encoder = keras.layers.Reshape(target_shape = (pod_dim * n_channels, ))\n",
    "encoder_block = [reshape_encoder, encoder_network]\n",
    "encoder_network = nns.ops.hstack_nns(\n",
    "    input_dims = (pod_dim, n_channels),\n",
    "    blocks = encoder_block\n",
    ")\n",
    "\n",
    "# Define decoder\n",
    "decoder_network = nns.DenseNetwork(\n",
    "    width = 30, \n",
    "    depth = 3, \n",
    "    output_dim = pod_dim * n_channels,\n",
    "    activation = 'elu',\n",
    "    kernel_initializer = 'he_uniform'\n",
    ")\n",
    "reshape_decoder = keras.layers.Reshape(target_shape = (pod_dim, n_channels))\n",
    "decoder_block = [decoder_network, reshape_decoder]\n",
    "decoder_network = nns.ops.hstack_nns(\n",
    "    input_dims = (latent_dim, ),\n",
    "    blocks = decoder_block\n",
    ")\n",
    "\n",
    "# Create a normalizer to normalize in-place the data\n",
    "normalizer = Normalizer(\n",
    "    input_train = train_data['mu'], \n",
    "    target_train = train_data['q_true']\n",
    ")\n",
    "\n",
    "# Create the DL-ROM architecture from the blocks \n",
    "# dlrom_training is the architecture used for training \n",
    "# dlrom_inference drops the encoder and is used for inference\n",
    "arch = dict()\n",
    "arch['reduced_network'] = reduced_network\n",
    "arch['encoder_network'] = encoder_network\n",
    "arch['decoder_network'] = decoder_network\n",
    "dlrom_training, dlrom_inference = nns.dlroms.create_dlrom(\n",
    "    input_dims = (p, ),\n",
    "    outputs_dims = (pod_dim, n_channels),\n",
    "    architecture = arch,\n",
    "    normalizer = normalizer\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom loss definition\n",
    "The library flexibility allows the user to easily define the custom loss functional, which in the case of POD-DL-ROM reads:\n",
    "\n",
    "\\begin{equation*}\n",
    "    \\mathcal{L}_{sup} = \\sum_{j} \\bigg\\|\\textnormal{dec} \\circ \\textnormal{red}(\\mu_j) - \\mathbf{q}(\\mu_j)\\bigg\\|^2 + \\sum_{j} \\bigg\\|\\textnormal{red}(\\mu_j) - \\textnormal{enc}(\\mathbf{q}(\\mu_j))\\bigg\\|^2.\n",
    "\\end{equation*}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomLoss(losses.LossFunction):\n",
    "\n",
    "    def __init__(self, dlrom,):\n",
    "        super(CustomLoss, self).__init__()\n",
    "        self.dlrom = dlrom\n",
    "    \n",
    "    def call(self, data, cache = None):\n",
    "        # Networks forward passes\n",
    "        # The attribute \"sup\" is mandatory and indicates that we use supervised \n",
    "        # (labeled) data\n",
    "        (\n",
    "            output,\n",
    "            latent_code,\n",
    "            reduced_output\n",
    "        ) = self.dlrom((data['mu_sup'], data['q_true_sup']))\n",
    "\n",
    "        # Loss computation\n",
    "        loss_rec = keras.ops.mean(\n",
    "            keras.ops.sum((output - data['q_true_sup'])**2, axis = 1)\n",
    "        )\n",
    "        loss_lat = keras.ops.mean(\n",
    "            keras.ops.sum((reduced_output - latent_code)**2, axis = 1)\n",
    "        )\n",
    "        loss = 0.5 * loss_rec + 0.5 * loss_lat\n",
    "\n",
    "        # Always return a dictionary consisting of losses, and other computed \n",
    "        # QoIs\n",
    "        return {'loss' : loss}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "Despite the simplicity of the user interface, in order to meet the user needs, we provide the user with a plethora of nice functionalities to set up the training.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:38<00:00,  5.26it/s, train_loss=2.89e-01, val_loss=1.13e-01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82e5352260>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the optimizer and compiling the model\n",
    "optimizer = keras.optimizers.Adam(learning_rate = 4e-4)\n",
    "trainer = Trainer(\n",
    "    loss_model = CustomLoss(\n",
    "        dlrom = dlrom_training\n",
    "    )\n",
    ")\n",
    "trainer.compile(optimizer = optimizer)\n",
    "\n",
    "# Set the results directory to store all relevant output files\n",
    "results_dir = os.path.join('results_tutorial', 'pod-dl-rom')\n",
    "if not os.path.exists(results_dir):\n",
    "    os.makedirs(results_dir)\n",
    "\n",
    "# Set the checkpoint folder to save weights and log files\n",
    "checkpoint = Checkpoint(save_folder = results_dir)\n",
    "log_filepath = checkpoint.get_log_filepath(name = 'pod-dl-rom')\n",
    "weights_filepath = checkpoint.get_weights_filepath(name = 'pod-dl-rom')\n",
    "log_callback = callbacks.LogCallback(log_filepath)\n",
    "save_callback = callbacks.SaveCallback(weights_filepath)\n",
    "load_callback = callbacks.LoadCallback(weights_filepath)\n",
    "\n",
    "# Train the model\n",
    "history = trainer.fit(\n",
    "    train_dataset = train_dataset,\n",
    "    val_dataset = val_dataset,\n",
    "    epochs = 200, \n",
    "    callbacks = [log_callback, save_callback, load_callback],\n",
    "    display_options = ('train_loss', 'val_loss')\n",
    ")\n",
    "\n",
    "# History contains the relevant quantities computed during the training\n",
    "# To be selected among the labels provided in display_options\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.semilogy(history.container['train_loss'], label = 'train loss')\n",
    "plt.semilogy(history.container['val_loss'], label = 'val loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Postprocessing\n",
    "\n",
    "We finally construct the full architecture by merging the neural network with the POD projection, namely, $\\mu \\mapsto \\mathbf{V} \\hat{\\mathbf{q}}(\\mu) = \\mathbf{V} (\\textnormal{dec} \\circ \\textnormal{red})(\\mu) \\approx \\mathbf{u}_h(\\mu)$.\n",
    "\n",
    "Finally, full compatibility with external libraries allows us to compute the accuracy of the chosen method and visualize the POD-DL-ROM prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean relative error = 1.534e-02\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82e5253fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the network to be used for the inference phase as a matrix-vector product\n",
    "# between the POD matrix and the coefficients inferred by the dlrom\n",
    "poddlrom_inference = nns.LowRankDecNetwork(\n",
    "    branch = dlrom_inference,\n",
    "    trunk = pod\n",
    ")\n",
    "\n",
    "# Compute the relative error\n",
    "mu_test = test_data['mu']\n",
    "u_true = test_data['u_true']\n",
    "u_pred = poddlrom_inference.predict(mu = mu_test).block_until_ready()\n",
    "mean_err = metrics.mean_rel_err_metric(u_true, u_pred)\n",
    "print('Mean relative error = %1.3e' % mean_err)\n",
    "\n",
    "# Visualize the solution for one instance of the physical parameter\n",
    "idx_plot = 0\n",
    "plt.figure(figsize = (11,5))\n",
    "plt.plot(X, u_pred[0], 'k', linewidth = 3, label = 'POD-DL-ROM')\n",
    "plt.plot(X, u_true[0], 'r--', linewidth = 2, label = 'Ground truth')\n",
    "plt.xlabel('$x$', fontsize = 15)\n",
    "plt.ylabel('$u(x;\\mu)$', fontsize = 15)\n",
    "plt.title('Comparison between POD-DL-ROM prediction and ground truth for $\\mu = $%1.3f' % mu_test[idx_plot][0])\n",
    "plt.legend()"
   ]
  }
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